{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "orig_nbformat": 4,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.6 64-bit ('base': conda)"
  },
  "interpreter": {
   "hash": "7b4c34fa5edc2b5c200e84280da452af41185f443fff3e767a73b82cf30c2550"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "from common.configs.path import paths\n",
    "from common.configs.stopwords import punc, stopwords\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "train_df = pd.read_csv(paths['train_data'])\n",
    "train_df.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(14009, 3)"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "train_df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7442 27878 9601 235 4004 10636 19121 28648 227...</td>\n",
       "      <td>5-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2281 24058 20163 22737 25572 1845 10446 28438 ...</td>\n",
       "      <td>5-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>12163 8224 13343 26307 18947 10922 10446 8679 ...</td>\n",
       "      <td>1-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>28685 16037 3261 14485 26363 16037 29555 8766 ...</td>\n",
       "      <td>1-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>10974 16249 8266 16328 2103 4198 929 4685 2254...</td>\n",
       "      <td>1-9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                               text label\n",
       "0   0  7442 27878 9601 235 4004 10636 19121 28648 227...  5-30\n",
       "1   1  2281 24058 20163 22737 25572 1845 10446 28438 ...  5-30\n",
       "2   2  12163 8224 13343 26307 18947 10922 10446 8679 ...   1-1\n",
       "3   3  28685 16037 3261 14485 26363 16037 29555 8766 ...   1-9\n",
       "4   4  10974 16249 8266 16328 2103 4198 929 4685 2254...   1-9"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# label number\n",
    "len(train_df['label'].value_counts())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "label_map = {i:j for i,j in zip(set(train_df['label'].tolist()), range(len(set(train_df['label'].tolist()))))}\n",
    "# label id\n",
    "train_df['label_id'] = train_df['label'].apply(lambda e: int(label_map[e]))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "with open(r'label_map.json', 'w') as f:\n",
    "    json.dump(label_map, f, indent=4)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# text length\n",
    "train_df['text_len'] = train_df['text'].apply(lambda e: len(e.split(' ')))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "train_df.loc[train_df['text_len']<64].shape[0]/train_df.shape[0]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.9620244128774359"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Text length Distribution"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "sns.set(rc={'figure.figsize':(15, 10)})\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "ax = sns.violinplot(x=train_df['text_len'])"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Label Distribution"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "train_df['label'].value_counts().sort_values().plot(kind = 'barh')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 15
    },
    {
     "output_type": "display_data",
     "data": {
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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# word count"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "test_df = pd.read_csv(paths['test_data'])\n",
    "test_df.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(6004, 2)"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "test_df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12163 8224 13343 25257 3470 10636 1317 26183 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>15912 10723 10300 10658 16328 2103 4198 ， 1216...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>17494 4198 14131 30100 18430 13688 4767 9614 8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15360 29731 9428 10238 104 4054 9531 3848 2006...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>12153 6837 26154 14489 25843 23120 26328 4198 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                               text\n",
       "0   0  12163 8224 13343 25257 3470 10636 1317 26183 1...\n",
       "1   1  15912 10723 10300 10658 16328 2103 4198 ， 1216...\n",
       "2   2  17494 4198 14131 30100 18430 13688 4767 9614 8...\n",
       "3   3  15360 29731 9428 10238 104 4054 9531 3848 2006...\n",
       "4   4  12153 6837 26154 14489 25843 23120 26328 4198 ..."
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "all_text = pd.concat([train_df['text'], test_df['text']])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "all_text_ser = all_text.str.split(expand=True).stack().value_counts()\n",
    "all_text_counter = all_text_ser.to_dict()\n",
    "with open(paths['output'] / 'char_count/train_test_count.json', 'w') as f:\n",
    "    json.dump(all_text_counter, f, indent=4)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "all_text_ser[:10]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "，        35341\n",
       "17281    19746\n",
       "19121    18817\n",
       "2281      9428\n",
       "25483     9105\n",
       "4198      7649\n",
       "15469     7127\n",
       "14547     7099\n",
       "929       7005\n",
       "9601      6689\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# pre-train data char count"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "with open(paths['output'] / 'tc_count.json', 'r') as f:\n",
    "    char_count = Counter(json.load(f))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "len(char_count)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "30365"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "char_count.most_common(20)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[('，', 663816658),\n",
       " ('17281', 457094392),\n",
       " ('16867', 272576970),\n",
       " ('。', 228154171),\n",
       " ('13535', 183904072),\n",
       " ('10449', 162304477),\n",
       " ('979', 128137119),\n",
       " ('4054', 127909733),\n",
       " ('20097', 108761106),\n",
       " ('2281', 105211937),\n",
       " ('23904', 103335999),\n",
       " ('1258', 85852365),\n",
       " ('14485', 77610986),\n",
       " ('30241', 74418276),\n",
       " ('16542', 71659920),\n",
       " ('10446', 71409454),\n",
       " ('9963', 61598238),\n",
       " ('26336', 60032705),\n",
       " ('12567', 59726648),\n",
       " ('10922', 58940489)]"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "# list out punctutation\n",
    "for k, v in dict(char_count).items():\n",
    "    if not re.findall('^[0-9]\\d*$', k):\n",
    "        print(k)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "，\n",
      "！\n",
      "。\n",
      "？\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "for l in tqdm(all_text.tolist()):\n",
    "    with open(paths['output'] / 'title_content.txt', 'a') as f:\n",
    "        f.write(l+'\\n\\n')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 20013/20013 [00:45<00:00, 437.15it/s]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "len(all_text.tolist()())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "20013"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# prepare word2vec text"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "source": [
    "all_text"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0       7442 27878 9601 235 4004 10636 19121 28648 227...\n",
       "1       2281 24058 20163 22737 25572 1845 10446 28438 ...\n",
       "2       12163 8224 13343 26307 18947 10922 10446 8679 ...\n",
       "3       28685 16037 3261 14485 26363 16037 29555 8766 ...\n",
       "4       10974 16249 8266 16328 2103 4198 929 4685 2254...\n",
       "                              ...                        \n",
       "5999    10539 16856 6762 27145 23904 16023 16023 23829...\n",
       "6000    20850 19551 12163 8224 13343 233 2103 16328 82...\n",
       "6001    19636 10645 23146 29976 27013 12196 3240 7800 ...\n",
       "6002    27427 25682 ， 20788 18600 27369 8644 12256 166...\n",
       "6003    20431 3848 15439 26526 24586 2281 26526 8194 1...\n",
       "Name: text, Length: 20013, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "punc"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['，', '。', '？', '！']"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "source": [
    "corpus = {'1gram': [],\n",
    "          '2gram': [],\n",
    "          '3gram': []}\n",
    "\n",
    "for line in all_text.tolist():\n",
    "    split_line = [re.sub('|'.join(stopwords), '', l.strip()) for l in re.split('|'.join(punc), line)]\n",
    "    for sl in split_line:\n",
    "        sl_list = (sl.strip()).split(' ')\n",
    "        sl_list = list(filter(lambda e: len(e)>0, sl_list))\n",
    "        sl_list = list(map(lambda e: e.strip(), sl_list))\n",
    "\n",
    "        corpus['1gram'].append(sl_list)\n",
    "        if len(sl_list) > 1:\n",
    "            n, m = 2, 1\n",
    "            sl_2gram = ['_'.join(sl_list[i:i+n]) for i in range(0, len(sl_list), n-m)][:-1]\n",
    "            corpus['2gram'].append(sl_2gram)\n",
    "        if len(sl_list) > 2:\n",
    "            n, m = 3, 2\n",
    "            sl_3gram = ['_'.join(sl_list[i:i+n]) for i in range(0, len(sl_list), n-m)][:-2]\n",
    "            corpus['3gram'].append(sl_3gram)"
   ],
   "outputs": [],
   "metadata": {
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "source": [
    "with open(paths['output'] / 'corpus/corpus_w2v_tt.json', 'w') as f:\n",
    "    json.dump(corpus, f, indent=4)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ]
}